Engine emissions compliance requires accurate detection of air-fuel imbalance between engine cylinders. Air-fuel imbalance between engine cylinders may occur due to various factors. For example, there may be cylinder-to-cylinder imbalance due to air leakages from some cylinders, exhaust gas recirculation errors, plugged intake valves, misfiring fuel injectors and faulty exhaust gas sensors. In addition to degrading emissions, air-fuel imbalances can reduce fuel efficiency and engine performance.
Cylinder-to-cylinder air-fuel imbalance may be monitored using an exhaust sensor to estimate an amount of air-fuel error by relating a sensor signal to a measured air-fuel deviation. One example approach of monitoring air-fuel variation in a multi-cylinder engine is described by Behr et al. in U.S. Pat. No. 7,802,563 B2. Therein, exhaust gas from a first group of cylinders is routed to an exhaust gas sensor, and during selected operating conditions, air-fuel imbalance is indicated in at least one of the cylinders based on a response of the exhaust gas sensor operating at or above firing frequency of cylinders in the first group. By indicating air-fuel imbalance in response to an exhaust gas sensor reading at or above firing frequency of the cylinders, feedback control interaction may be isolated to achieve a consistent indication of air-fuel error.
However, the inventors herein have recognized potential issues with such a system for air fuel imbalance detection. For example, poor or insufficient mixing of exhaust gas at an exhaust gas sensor may create discrepancies in sensor readings. As such, air-fuel error estimates made under such exhaust mixing conditions may not reflect the actual cylinder imbalance. Furthermore, exhaust system geometry may create additional issues with air-fuel imbalance learning. For example, in a multi-cylinder engine, due to stratified flow and non-uniform mixing of flow from cylinders, the flow from some cylinders may be masked from the exhaust gas sensor by the flow from other cylinders. As a result, there may be some cylinders whose flow never passes through the exhaust gas sensor. Another shortcoming may be reduced sensitivity of the exhaust gas sensor during certain engine operating conditions. For example, during cold-start conditions, the exhaust gas sensor may not be sufficiently warmed up and may register sensor readings with discrepancies, affecting cylinder air-fuel imbalance learning.
In alternate approaches, the air-fuel imbalance may be learned using in-cylinder pressure or torque errors. However such sensors may be expensive. Still other approaches rely on exhaust pressure sensors. However, such sensors may be unreliable especially when the pressure is measured in the exhaust manifold further downstream from the cylinder output. Still other approaches may intrusively drive engine cylinders very lean or very rich to identify the imbalance. However, such intrusive approaches can result in excessive emissions.
In one example, the shortcomings described above may be at least partly addressed by a method for an engine that comprises: during a cylinder deactivation event, sequentially deactivating each cylinder of a cylinder group including two or more cylinders; estimating a lambda deviation for each cylinder following the sequential deactivation of each cylinder of the cylinder group; and learning an air error for each cylinder based on the estimated lambda deviation. In this way, the air error in cylinders of a multi-cylinder engine may be reliably and opportunistically identified while accounting for discrepancies created by exhaust geometry, sensor sensitivity and exhaust mixing.
As one example, an engine may include a plurality of cylinders located in a first and a second cylinder bank. During conditions when the engine load is low, one or more cylinders, such as all cylinders of one cylinder bank, may be selectively deactivated (e.g., fuel and spark may be deactivated) while the remaining active cylinders are operated with a higher average load to reduce engine pumping losses and improve fuel economy. Prior to cylinder deactivation, an air-fuel ratio with all cylinders firing may be noted. During the cylinder deactivation event, the cylinders to be deactivated may be sequentially deactivated and a lambda deviation (from the air-fuel ratio with all cylinders firing) for each cylinder following the sequential deactivation may be determined. Since the deactivated cylinder is not receiving fuel, any lambda deviation is attributed to air flowing through the cylinder. In this way, the air error for each cylinder may be learned. Additionally, the lambda deviation may be compared to an expected lambda deviation to learn an air error for each cylinder. An order of cylinder deactivation may be adjusted so that the air error for each engine cylinder can be learned during the deactivation event. The learned air errors can then be used to determine an air-fuel imbalance between cylinders. By learning the air error in each cylinder of the first and second cylinder bank based on the estimated lambda deviation, issues related to exhaust geometry, sensor sensitivity and exhaust mixing may be addressed.
The approach described here may confer several advantages. For example, the method provides improved learning of air-fuel imbalance between cylinders of a multi-cylinder engine. By deactivating each cylinder of a cylinder group opportunistically during a cylinder deactivation mode of engine operation while the remaining engine cylinders are active, individual cylinder air errors may be learned independent of exhaust manifold geometry, and even in the presence of non-uniform cylinder flow. Furthermore, cylinder imbalance can be reliably determined using an existing exhaust sensor. By learning the air-fuel imbalance between cylinders, engine operation can be adjusted to account for and/or compensate for said imbalance. As such, by reducing cylinder-to-cylinder air-fuel variations in an engine, exhaust emissions may be reduced and fuel efficiency may be improved.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.